As noted in Program Announcement # PAR-08-213, Methodology and Measurement in the Behavioral and Social Sciences, there is a need for "developing appropriate analytic techniques for use with new kinds of data and new approaches to behavioral and social science research." This proposal is aimed at addressing this need for data generated from diary or Ecological Momentary Assessment (EMA) methods. Use of EMA methods in smoking and cancer research has become a new and vital approach. Data from EMA studies are inherently multilevel in nature with, for example, (level-1) observations nested within (level-2) days and (level-1) subjects. Thus, linear mixed models (LMMs, aka multilevel or hierarchical linear models) are increasingly used for analysis of EMA data. In EMA studies, it is not unusual for there to be up to thirty or forty observations per subject, and this allows greater modeling opportunities than what conventional LMMs for longitudinal data allow. In particular, one very promising extended approach is the modeling of variances as a function of covariates, in addition to their effect on overall mean levels. For example, if a smoker's mood is the outcome, then one can consider the effect of covariates on their mood level (e.g., how happy/sad are they on average), as well as on their variation in mood (e.g., how labile/erratic is their mood). Or, one can examine mood changes when a person smokes in terms of the mean (does mood improve?) and variance (does mood stabilize?), and what variables might be related to those smoking-related changes of mood level and variation. Thus, by allowing within-subject variance to be a function of covariates, we can more directly examine the hypothesis that smoking helps to regulate mood. Thus, the aims of the proposed study are to (1) develop accessible software for general 3-level modeling of means and variances of EMA data;and (2) examine the role of smoking on mood regulation in adolescents using data from our program project grant, "Social and Emotional Contexts of Adolescent Smoking Patterns" (NCI grant #PO1 CA98262), which established a cohort of adolescents at high risk for the development of smoking and nicotine dependence. This study has the potential to make notable methodological and substantive contributions for analysis of EMA data and understanding the relationship between mood variation and smoking dependency. These methods can easily generalize to a variety of cancer -relevant research areas, including the assessment of pain and symptoms, as well as diet and exercise. PUBLIC HEALTH RELEVANCE: Use of Ecological Momentary Assessment (EMA) methods in smoking and cancer research has become a new and vital approach, allowing for the examination of smoking-related phenomena as they happen over time. As noted in Program Announcement # PAR-08-213, Methodology and Measurement in the Behavioral and Social Sciences, there is a need for "developing appropriate analytic techniques for use with new kinds of data and new approaches to behavioral and social science research." This proposal is aimed at addressing this need by developing statistical methods and software for EMA data consisting of observations nested within days and subjects, allowing for effects on both average levels (is the variable consistently higher or lower) and levels of variation (is the variable more labile or erratic). With the new focus on variation, the proposed research will examine previously un-addressable questions in smoking research.